scholarly journals Factors Affecting the Quality of Person-Generated Wearable Device Data and Associated Challenges: Rapid Systematic Review

10.2196/20738 ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. e20738
Author(s):  
Sylvia Cho ◽  
Ipek Ensari ◽  
Chunhua Weng ◽  
Michael G Kahn ◽  
Karthik Natarajan
Keyword(s):  
Data Quality ◽  
Wearable Devices ◽  
Heterogeneous Data ◽  
Wearable Device ◽  
Bring Your Own Device ◽  
Contributing Factors ◽  
Data Governance ◽  
Related Factors ◽  
Factors Affecting

Background There is increasing interest in reusing person-generated wearable device data for research purposes, which raises concerns about data quality. However, the amount of literature on data quality challenges, specifically those for person-generated wearable device data, is sparse. Objective This study aims to systematically review the literature on factors affecting the quality of person-generated wearable device data and their associated intrinsic data quality challenges for research. Methods The literature was searched in the PubMed, Association for Computing Machinery, Institute of Electrical and Electronics Engineers, and Google Scholar databases by using search terms related to wearable devices and data quality. By using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, studies were reviewed to identify factors affecting the quality of wearable device data. Studies were eligible if they included content on the data quality of wearable devices, such as fitness trackers and sleep monitors. Both research-grade and consumer-grade wearable devices were included in the review. Relevant content was annotated and iteratively categorized into semantically similar factors until a consensus was reached. If any data quality challenges were mentioned in the study, those contents were extracted and categorized as well. Results A total of 19 papers were included in this review. We identified three high-level factors that affect data quality—device- and technical-related factors, user-related factors, and data governance-related factors. Device- and technical-related factors include problems with hardware, software, and the connectivity of the device; user-related factors include device nonwear and user error; and data governance-related factors include a lack of standardization. The identified factors can potentially lead to intrinsic data quality challenges, such as incomplete, incorrect, and heterogeneous data. Although missing and incorrect data are widely known data quality challenges for wearable devices, the heterogeneity of data is another aspect of data quality that should be considered for wearable devices. Heterogeneity in wearable device data exists at three levels: heterogeneity in data generated by a single person using a single device (within-person heterogeneity); heterogeneity in data generated by multiple people who use the same brand, model, and version of a device (between-person heterogeneity); and heterogeneity in data generated from multiple people using different devices (between-person heterogeneity), which would apply especially to data collected under a bring-your-own-device policy. Conclusions Our study identifies potential intrinsic data quality challenges that could occur when analyzing wearable device data for research and three major contributing factors for these challenges. As poor data quality can compromise the reliability and accuracy of research results, further investigation is needed on how to address the data quality challenges of wearable devices.

scholarly journals Factors Affecting the Quality of Person-Generated Wearable Device Data and Associated Challenges: Rapid Review (Preprint)

2020 ◽  
Author(s):  
Sylvia Cho ◽  
Ipek Ensari ◽  
Chunhua Weng ◽  
Michael Kahn ◽  
Karthik Natarajan
Keyword(s):  
Data Quality ◽  
Heterogeneous Data ◽  
Wearable Device ◽  
Rapid Review ◽  
Data Governance ◽  
Related Factors ◽  
Data Accessibility ◽  
Factors Affecting ◽  
Address Data

BACKGROUND There is increasing interest to reuse person-generated wearable device data for research purposes, which raises concerns about data quality. However, the literature on data quality challenges, specifically for person-generated wearable device data, is sparse. OBJECTIVE The objective of this study is to systematically review the literature on factors affecting quality of person-generated wearable device data and identify challenges associated with their secondary uses. METHODS We searched PubMed, ACM, IEEE, and Google Scholar with search terms related to wearable device and data quality. Using PRISMA guidelines, we reviewed the papers to find factors affecting the quality of wearable device data. We annotated those papers and categorized semantically similar factors. If any data quality challenges were mentioned in the study, we extracted those contents as well. RESULTS Twenty-six papers were included. We identified 3 high-level factors: device and technical, user-related, and data governance factors. Device and technical factors include problems with hardware, software, connectivity; user-related factors include device non-wear and user error; and data governance factors include lack of standardization and data accessibility issues. The identified factors potentially can lead to data quality problems such as incomplete, inaccurate, and heterogeneous data. CONCLUSIONS Our study identifies potential data quality challenges that could occur when analyzing wearable device data for research and 3 major contributing factors for these challenges. As poor data quality can compromise the reliability and accuracy of research results, further investigation is warranted on how to address data quality challenges facing wearable devices.

Quality of life in breast cancer sufferers

2016 ◽  
Vol 29 (7) ◽  
pp. 721-732 ◽  
Author(s):  
Ahmed Essmat Shouman ◽  
Nahla Fawzy Abou El Ezz ◽  
Nivine Gado ◽  
Amal Mahmoud Ibrahim Goda
Keyword(s):  
Breast Cancer ◽  
Quality Of Life ◽  
Family Income ◽  
Early Stage ◽  
Functional Domain ◽  
Related Factors ◽  
Specific Patient ◽  
Content Type ◽  
Factors Affecting

Purpose – The purpose of this paper is to measure health-related quality of life (QOL) among patients with early stage cancer breast under curative treatment at department of oncology and nuclear medicine at Ain Shams University Hospitals. Identify factors affecting QOL among these patients. Design/methodology/approach – A cross-sectional study measured QOL among early stage female breast cancer (BC) patients and determined the main factors affecting their QOL. Three interviewer administered questionnaires were used. Findings – The physical domain mostly affected in BC patients and the functional domain least. Socio-demographic factors that significantly affected BC patients QOL scores were patient age, education, having children and family income. Specific patient characteristics include caregiver presence – a factor that affected different QOL scores. Age at diagnosis, affection in the side of the predominant hand, post-operative chemotherapy and difficulty in obtaining the medication were the disease-related factors that affected QOL scores. Originality/value – The final model predicting QOL for early stage female BC patients included age, education and difficulty in obtaining the medication as determinants for total QOL score. Carer presence was the specific patient characteristic that affected different QOL scores.

scholarly journals Quality of Open Research Data: Values, Convergences and Governance

Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 175 ◽  
Author(s):  
Tibor Koltay
Keyword(s):  
Big Data ◽  
Data Quality ◽  
Academic Research ◽  
Research Data ◽  
Data Governance ◽  
Quality Of Data ◽  
Open Research ◽  
Research Environments

This paper focuses on the characteristics of research data quality, and aims to cover the most important issues related to it, giving particular attention to its attributes and to data governance. The corporate word’s considerable interest in the quality of data is obvious in several thoughts and issues reported in business-related publications, even if there are apparent differences between values and approaches to data in corporate and in academic (research) environments. The paper also takes into consideration that addressing data quality would be unimaginable without considering big data.

Study on the feasibility of data sharing and collecting consumer wearable and mobile survey data to assess physical and mental health status : data quality and study challenges using an opt-in panel

2020 ◽  
Author(s):  
◽  
Adam Bouras
Keyword(s):  
Data Quality ◽  
Wearable Devices ◽  
Health Data ◽  
Wearable Device ◽  
Physical And Mental Health ◽  
White Mothers ◽  
Status Data ◽  
The Impact ◽  
Mobile Survey ◽  
Hispanic White

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI-COLUMBIA AT REQUEST OF AUTHOR.] The use of opt-in panel for health research and smartphones are still in their infancy, and the impact of how opt-in panel members share their health data for a different purpose for research is not yet well explored more specifically data from consumer wearable devices. Thus, we implemented the eCaregiving study, a two-phase feasibility study, to assesses opt-in panel members' behavior to share their health data with researchers and establish a linkage between consumer wearable devices data and self-reported outcome. The first phase was about assessing opt-in panel members to share their patient health data and their interest to participate in sharing their wearable devices' data using a survey questionnaire -- the panel is composed of healthy non-Hispanic white mothers. The second phase of eCaregiving was to recruit those who expressed interest in sharing their wearable device data and participate in the self-reported outcome mobile survey questionnaire. We grouped our participants into those who use Fitbit and those who do not use any wearable devices, and the later was given a Fitbit Charger HR as an incentive for their participation. Although we targeted fifty participants from each group, we were able to recruit only five participants from those who use Fitbit, and we achieved our target for those who never used any wearable device. The feasibility study showed that the interest to participate in the study did not translate into actual participation. Although we gave incentives to these participants, we found a discrepancy in the actual participation, and this discrepancy warranted further studies to determine the exact reasons for non-participation. Throughout this study, our participants received minimal guidance and training on how to use wearables devices or how to synchronize their device with the mobile application -- e4 research app. We found that mobile survey has better participation, attrition, and completion rate and completion time than the traditional surveys. We also investigated the data quality from the consumer wearable device, and we found that number of days captured of step count is significant. We also found that the number of sleep hours captured is low, but they are better than another controlled study where the participants have trained to use these consumer wearable devices. All in all, our study can be used as a guideline for future studies on mhealth and wearable devices to develop efficient protocols to maximize data quality from wearable devices and mobile surveys. The study provides a systematic approach to recruit and link subjective and objective data for more actionable insight. Besides, we reported the impact of incentives on the participation rate and the attrition rate in mobile surveys. Overall, mobile surveys and wearable devices can complement each other and enhance our understanding of the overall daily activity of our participants. The remaining of this thesis is structured as follows; the first chapter introduces the first paper entitled non-Hispanic white mothers' willingness to share personal health data with researchers: survey results from an opt-in panel. The final chapter introduces the second paper entitled study on the feasibility of collecting consumer wearable and mobile survey data to assess physical and mental health status -- data quality and study chall

scholarly journals Patients’ Concerns at Their Admission in Hospital and Its Related Factors in Kermanshah, Iran

2021 ◽  
Vol 10 (1) ◽  
pp. 23-28
Author(s):  
Soraya Siabani ◽  
Leila Solouki ◽  
Afshin Almasi ◽  
Sina Siabani ◽  
Motahareh Khaledi ◽  
...  
Keyword(s):  
Hospital Costs ◽  
Length Of Hospital Stay ◽  
Final Diagnosis ◽  
Medical Sciences ◽  
Related Factors ◽  
Factors Affecting ◽  
Hospital Administrators ◽  
Multi Stage ◽  
Significant Difference

Background: One of the critical factors affecting patients’ outcomes is their concerns about different issues during their admission to the hospital. Clarifying these concerns and providing appropriate approaches could improve the quality of care, result in better outcomes, and reduce treatment costs. The present study aimed to investigate patients’ concerns during hospitalization, and the likely related factors of the educational hospitals in Kermanshah, western Iran. Materials and Methods: This analytical-descriptive study included 600 adult patients selected via a multi-stage sampling method and admitted to all four educational hospitals affiliated to Kermanshah University of Medical Sciences )KUMS) in 2016. Required data were collected using a survey with 15 questions on demographic information, current disease, medical records, and a researcher-developed questionnaire on factors causing concern in the Likert scale. Results: Of 600 patients who participated in the survey, 336 (56%) were female and 486 )81%) were married. The most frequent concerns were the length of admission, failure in treatment or recovery, and hospital costs, respectively. The length of hospital stay, income, and level of education were significantly associated with the concern scores. Also, there was a significant difference between concern score distributions in groups with a definite diagnosis of illnesses (P<0.05). Conclusion: The results of this study suggested a correlation between variables such as education, income, the final diagnosis of a sickness, and the concern level of admitted patients. Our findings could help managers and hospital administrators better understand the concerns of admitted patients and find solutions to remove them.

scholarly journals Supportive Therapy for Fatigue in Hemodialysis Patients

2021 ◽  
Vol 11 (7) ◽  
pp. 367-373
Author(s):  
Ramai P ◽  
Diana Lobo
Keyword(s):  
Quality Of Life ◽  
Health Care ◽  
Supportive Therapy ◽  
Health Related Quality ◽  
Dialysis Patients ◽  
Contributing Factors ◽  
Related Factors ◽  
Related Quality ◽  
Health Related

Fatigue is an enervating symptom of prolonged dialysis of patients and significantly impacts the health related quality of life of dialysis patients. Reduction of fatigue in dialysis patients is a challenging task for any health care provider. Fatigue develops during long term dialysis usually due to chronic health conditions associated with prolonged dialysis. The contributing factors for fatigue in end stage renal disease (ESRD) patients may be broadly classified into physiological, psychological / behavioural, socio-demographic and dialysis related factors. It is known that some of these factors are modifiable leading to reduction in fatigue. A multidisciplinary health care strategy comprising alternative therapy such as acupressure; mind based therapy such as meditation, deep breathing and yoga; body based therapies such as physical activity, therapeutic exercise, body massage; biological based therapy such as diet and nutrition shall help to reduce the fatigue in dialysis population. Conclusion: To improve the patient care and health related quality of life in dialysis patients, nurses should develop a framework for decreasing the fatigue. This concept paper describes the various therapies available for reducing the fatigue and discusses the ways of including these supportive and alternative therapies into regular medical care. Key words: Fatigue, ESRD, hemodialysis, alternative and supportive therapy

scholarly journals Monitoring and Predicting Occupant’s Sleep Quality by Using Wearable Device OURA Ring and Smart Building Sensors Data (Living Laboratory Case Study)

Buildings ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 459
Author(s):  
Elena Malakhatka ◽  
Anas Al Al Rahis ◽  
Osman Osman ◽  
Per Lundqvist
Keyword(s):  
Sleep Quality ◽  
Ambient Air ◽  
Wearable Devices ◽  
Wearable Device ◽  
Physiological Data ◽  
Smart Meters ◽  
Smart Building ◽  
Good Potential ◽  
Indoor Comfort

Today’s commercially-off-the-shelf (COST) wearable devices can unobtrusively capture several important parameters that may be used to measure the indoor comfort of building occupants, including ambient air temperature, relative humidity, skin temperature, perspiration rate, and heart rate. These data could be used not only for improving personal wellbeing, but for adjusting a better indoor environment condition. In this study, we have focused specifically on the sleeping phase. The main purpose of this work was to use the data from wearable devices and smart meters to improve the sleep quality of residents living at KTH Live-in-Lab. The wearable device we used was the OURA ring which specializes in sleep monitoring. In general, the data quality showed good potential for the modelling phase. For the modelling phase, we had to make some choices, such as the programming language and the AI algorithm, that was the best fit for our project. First, it aims to make personal physiological data related studies more transparent. Secondly, the tenants will have a better sleep quality in their everyday life if they have an accurate prediction of the sleeping scores and ability to adjust the built environment. Additionally, using knowledge about end users can help the building owners to design better building systems and services related to the end-user’s wellbeing.

Analyzing the Factors Affecting the Quality of IoT-based Smart Wearable Devices Using the DANP Method

2019 ◽  
Vol 28 (4) ◽  
pp. 431-442
Author(s):  
Alexandru BALOG ◽  
Lidia BĂJENARU ◽  
Irina CRISTESCU
Keyword(s):  
Wearable Devices ◽  
Factors Affecting ◽  
Smart Wearable

scholarly journals Wearable Devices for Biofeedback Rehabilitation: A Systematic Review and Meta-Analysis to Design Application Rules and Estimate the Effectiveness on Balance and Gait Outcomes in Neurological Diseases

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3444
Author(s):  
Thomas Bowman ◽  
Elisa Gervasoni ◽  
Chiara Arienti ◽  
Stefano Giuseppe Lazzerini ◽  
Stefano Negrini ◽  
...  
Keyword(s):  
Systematic Review ◽  
Positive Reinforcement ◽  
Neurological Diseases ◽  
Dynamic Balance ◽  
Meta Analysis ◽  
Wearable Devices ◽  
Wearable Device ◽  
Positive Effects ◽  
Body Parameters

Wearable devices are used in rehabilitation to provide biofeedback about biomechanical or physiological body parameters to improve outcomes in people with neurological diseases. This is a promising approach that influences motor learning and patients’ engagement. Nevertheless, it is not yet clear what the most commonly used sensor configurations are, and it is also not clear which biofeedback components are used for which pathology. To explore these aspects and estimate the effectiveness of wearable device biofeedback rehabilitation on balance and gait, we conducted a systematic review by electronic search on MEDLINE, PubMed, Web of Science, PEDro, and the Cochrane CENTRAL from inception to January 2020. Nineteen randomized controlled trials were included (Parkinson’s n = 6; stroke n = 13; mild cognitive impairment n = 1). Wearable devices mostly provided real-time biofeedback during exercise, using biomechanical sensors and a positive reinforcement feedback strategy through auditory or visual modes. Some notable points that could be improved were identified in the included studies; these were helpful in providing practical design rules to maximize the prospective of wearable device biofeedback rehabilitation. Due to the current quality of the literature, it was not possible to achieve firm conclusions about the effectiveness of wearable device biofeedback rehabilitation. However, wearable device biofeedback rehabilitation seems to provide positive effects on dynamic balance and gait for PwND, but higher-quality RCTs with larger sample sizes are needed for stronger conclusions.

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